code: def test_remove_highly_correlated_features_init_woodwork():
      correlated_df = pd.DataFrame(
          {
              "id": [0, 1, 2, 3],
              "diff_ints": [34, 11, 29, 91],
              "words": [
                  "test",
                  "this is a short sentence",
                  "foo bar",
                  "baz",
              ],
              "corr_words": [4, 24, 7, 3],
              "corr_1": [99, 88, 77, 33],
              "corr_2": [99, 88, 77, 33],
          },
      )
  
      es = EntitySet(
          "data",
          {
              "correlated": (
                  correlated_df,
                  "id",
                  None,
                  {"words": NaturalLanguage},
              )
          },
      )
      fm, _ = dfs(
          entityset=es,
          target_dataframe_name="correlated",
          trans_primitives=["num_characters"],
          max_depth=1,
      )
  
      no_ww_fm = fm.copy()
      ww_fm = fm.copy()
      ww_fm.ww.init()
  
      new_no_ww_fm = remove_highly_correlated_features(no_ww_fm)
      new_ww_fm = remove_highly_correlated_features(ww_fm)
  
      pd.testing.assert_frame_equal(new_no_ww_fm, new_ww_fm)
  
file_path: featuretools/featuretools/tests/selection/test_selection.py
imports: import numpy
  import pandas
  import pytest
  from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import Boolean, BooleanNullable, NaturalLanguage
  from featuretools import EntitySet, Feature, dfs
  from featuretools.selection import remove_highly_correlated_features, remove_highly_null_features, remove_low_information_features, remove_single_value_features
  from featuretools.tests.testing_utils import make_ecommerce_entityset
label_desc: Use image node_id 8 for calling a global function with example usage: test_remove_highly_correlated_features_init_woodwork() without return types